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Taxonomy of Big Data Stores

Nontraditional databases have grown tremendously in the past few years. Now we have literally a few hundred Big Data stores and more are coming. The upside is that all of these drive innovations and lower the cost for customers. The downside is that it is easy for an end-user to get swamped with so many choices and sometimes become lost. It is important to classify these Big Data stores in such a way that one can sort them out and find the best match swiftly in the selection process.

A matrix is developed to group various options available in the market. It has 2 dimensions: Horizontal and Vertical. The horizontal dimension is intended to segregate the underlying methods for the data stores. The vertical dimension deals with the complexity and sophistication of the solutions.

Horizontally, there are 3 groups specified: SQL, NewSQL, and NoSQL.
  • SQL stores are conventional RDBMS supporting Structured Query Language. 
  • The NewSQL stores extend the SQL capability by bypassing or working around the inherent limitation of RDBMS.
  • NoSQL stores follow BASE in place of ACID.
Vertically, 3 buckets are set: Modest, Sophisticated, and Advanced.
  • Modest: basic functions in a simple form
  • Sophisticated: more complex structure and data model
  • Advanced: leading-edge solutions and converged kinds
The 3X3 matrix is shown in the illustrative diagram. It comprises 9 cells in the map. Different store types are listed in each cell. In total, 27 categories are defined in the taxonomy. As an example, the Modest NoSQL group entails the key-value, columnar, and document-oriented data stores, including alternative file system for Hadoop components.

For more information, please contact Tony Shan (blog@tonyshan.com). ©Tony Shan. All rights reserved.

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Tony Shan works as a senior consultant, advisor at a global applications and infrastructure solutions firm helping clients realize the greatest value from their IT. Shan is a renowned thought leader and technology visionary with a number of years of field experience and guru-level expertise on cloud computing, Big Data, Hadoop, NoSQL, social, mobile, SOA, BI, technology strategy, IT roadmapping, systems design, architecture engineering, portfolio rationalization, product development, asset management, strategic planning, process standardization, and Web 2.0. He has directed the lifecycle R&D and buildout of large-scale award-winning distributed systems on diverse platforms in Fortune 100 companies and public sector like IBM, Bank of America, Wells Fargo, Cisco, Honeywell, Abbott, etc.

Shan is an inventive expert with a proven track record of influential innovations such as Cloud Engineering. He has authored dozens of top-notch technical papers on next-generation technologies and over ten books that won multiple awards. He is a frequent keynote speaker and Chair/Panel/Advisor/Judge/Organizing Committee in prominent conferences/workshops, an editor/editorial advisory board member of IT research journals/books, and a founder of several user groups, forums, and centers of excellence (CoE).